An image can be segmented by classifying its pixels using local properties as features. Two intuitively useful properties are the gray level of the pixel and the ``busyness,'' or gray level fluctuation, measured in its neighborhood. Busyness values tend to be highly vari-able in busy regions; but great improvements in classification accuracy can be obtained by smoothing these values prior to classifying. An alternative possibility is to classify probabilistically and use relaxation to adjust the probabilities.